To validate the dynamic scale property of our CDSFNet, we measure depth precision on different image resolutions. Depth precision is the average percentage of depths with errors lower than a defined threshold [1]}. Here we computed precision with the thresholds of 2mm and 4mm. We compared to CasMVSNet, PatchMatchNet, UCSNet, and CVP-MVSNet, which applied the cascade structure to predict high-resolution depths. Note that all methods are trained on the datasets with image-resolution of \(640 \times 512\) . Table REF  presents the quantitative results for the depth estimated on five image resolutions. The baseline methods degraded the performance when the image-resolution was increased while our method could maintain a stable performance at any image resolution. The reason is that our feature extraction CDSFNet can utilize dynamic scales for learning features. The dynamic scale adapts to various image-resolutions and object scales in image.
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